Lab 12 Homework
Zayna Siddiqi
2022-03-01
Instructions
Answer the following questions and complete the exercises in RMarkdown. Please embed all of your code and push your final work to your repository. Your final lab report should be organized, clean, and run free from errors. Remember, you must remove the # for the included code chunks to run. Be sure to add your name to the author header above. For any included plots, make sure they are clearly labeled. You are free to use any plot type that you feel best communicates the results of your analysis.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
Load the libraries
library(tidyverse)
library(janitor)
library(here)
library(ggmap)Load the Data
We will use two separate data sets for this homework.
The first data set represent sightings of grizzly bears (Ursos arctos) in Alaska.
The second data set is from Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
Load the
grizzlydata and evaluate its structure. As part of this step, produce a summary that provides the range of latitude and longitude so you can build an appropriate bounding box.
grizzly <- read_csv(here("lab12", "data", "bear-sightings.csv"))## Rows: 494 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): bear.id, longitude, latitude
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(grizzly)## Rows: 494
## Columns: 3
## $ bear.id <dbl> 7, 57, 69, 75, 104, 108, 115, 116, 125, 135, 137, 162, 185, …
## $ longitude <dbl> -148.9560, -152.6228, -144.9374, -152.8485, -143.2948, -149.…
## $ latitude <dbl> 62.65822, 58.35064, 62.38227, 59.90122, 61.07311, 62.91605, …- Use the range of the latitude and longitude to build an appropriate bounding box for your map.
lat <- c(55.02, 70.37)
long <- c(-131.3, -166.2)
bbox <- make_bbox(long, lat, f = 0.05)- Load a map from
stamenin a terrain style projection and display the map.
map1 <- get_map(bbox, maptype = "terrain", source = "stamen")## Source : http://tile.stamen.com/terrain/5/1/6.png## Source : http://tile.stamen.com/terrain/5/2/6.png## Source : http://tile.stamen.com/terrain/5/3/6.png## Source : http://tile.stamen.com/terrain/5/4/6.png## Source : http://tile.stamen.com/terrain/5/1/7.png## Source : http://tile.stamen.com/terrain/5/2/7.png## Source : http://tile.stamen.com/terrain/5/3/7.png## Source : http://tile.stamen.com/terrain/5/4/7.png## Source : http://tile.stamen.com/terrain/5/1/8.png## Source : http://tile.stamen.com/terrain/5/2/8.png## Source : http://tile.stamen.com/terrain/5/3/8.png## Source : http://tile.stamen.com/terrain/5/4/8.png## Source : http://tile.stamen.com/terrain/5/1/9.png## Source : http://tile.stamen.com/terrain/5/2/9.png## Source : http://tile.stamen.com/terrain/5/3/9.png## Source : http://tile.stamen.com/terrain/5/4/9.png## Source : http://tile.stamen.com/terrain/5/1/10.png## Source : http://tile.stamen.com/terrain/5/2/10.png## Source : http://tile.stamen.com/terrain/5/3/10.png## Source : http://tile.stamen.com/terrain/5/4/10.pngggmap(map1)
- Build a final map that overlays the recorded observations of grizzly bears in Alaska.
ggmap(map1) +
geom_point(data = grizzly, aes(longitude, latitude), size=0.8) +
labs(x = "Longitude", y = "Latitude", title = "Grizzly bears")
Let’s switch to the wolves data. Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
- Load the data and evaluate its structure.
wolves <- read_csv(here("lab12", "data", "wolves_data", "wolves_dataset.csv"))## Rows: 1986 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): pop, age.cat, sex, color
## dbl (19): year, lat, long, habitat, human, pop.density, pack.size, standard....
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(wolves)## Rows: 1,986
## Columns: 23
## $ pop <chr> "AK.PEN", "AK.PEN", "AK.PEN", "AK.PEN", "AK.PEN", "…
## $ year <dbl> 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 200…
## $ age.cat <chr> "S", "S", "A", "S", "A", "A", "A", "P", "S", "P", "…
## $ sex <chr> "F", "M", "F", "M", "M", "M", "F", "M", "F", "M", "…
## $ color <chr> "G", "G", "G", "B", "B", "G", "G", "G", "G", "G", "…
## $ lat <dbl> 57.03983, 57.03983, 57.03983, 57.03983, 57.03983, 5…
## $ long <dbl> -157.8427, -157.8427, -157.8427, -157.8427, -157.84…
## $ habitat <dbl> 254.08, 254.08, 254.08, 254.08, 254.08, 254.08, 254…
## $ human <dbl> 10.42, 10.42, 10.42, 10.42, 10.42, 10.42, 10.42, 10…
## $ pop.density <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, …
## $ pack.size <dbl> 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.7…
## $ standard.habitat <dbl> -1.6339, -1.6339, -1.6339, -1.6339, -1.6339, -1.633…
## $ standard.human <dbl> -0.9784, -0.9784, -0.9784, -0.9784, -0.9784, -0.978…
## $ standard.pop <dbl> -0.6827, -0.6827, -0.6827, -0.6827, -0.6827, -0.682…
## $ standard.packsize <dbl> 1.3157, 1.3157, 1.3157, 1.3157, 1.3157, 1.3157, 1.3…
## $ standard.latitude <dbl> 0.7214, 0.7214, 0.7214, 0.7214, 0.7214, 0.7214, 0.7…
## $ standard.longitude <dbl> -2.1441, -2.1441, -2.1441, -2.1441, -2.1441, -2.144…
## $ cav.binary <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ cdv.binary <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cpv.binary <dbl> 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ chv.binary <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, …
## $ neo.binary <dbl> NA, NA, NA, 0, 0, NA, NA, 1, 0, 1, NA, 0, NA, NA, N…
## $ toxo.binary <dbl> NA, NA, NA, 1, 0, NA, NA, 1, 0, 0, NA, 0, NA, NA, N…- How many distinct wolf populations are included in this study? Mae a new object that restricts the data to the wolf populations in the lower 48 US states.
wolves %>% count(pop)## # A tibble: 17 × 2
## pop n
## <chr> <int>
## 1 AK.PEN 100
## 2 BAN.JAS 96
## 3 BC 145
## 4 DENALI 154
## 5 ELLES 11
## 6 GTNP 60
## 7 INT.AK 35
## 8 MEXICAN 181
## 9 MI 102
## 10 MT 351
## 11 N.NWT 67
## 12 ONT 60
## 13 SE.AK 10
## 14 SNF 92
## 15 SS.NWT 34
## 16 YNP 383
## 17 YUCH 105us_wolves <-
wolves %>%
filter(pop %in% c("GTNP", "MEXICAN", "MI", "MT", "SNF", "YNP"))- Use the range of the latitude and longitude to build an appropriate bounding box for your map.
us_wolves %>%
select(lat, long) %>%
summary()## lat long
## Min. :33.89 Min. :-110.99
## 1st Qu.:44.60 1st Qu.:-110.99
## Median :44.60 Median :-110.55
## Mean :43.95 Mean :-106.91
## 3rd Qu.:46.83 3rd Qu.:-109.17
## Max. :47.75 Max. : -86.82lat <- c(33.69, 47.75)
long <- c(-110.99, -86.82)
bbox2 <- make_bbox(long, lat, f = 0.05)- Load a map from
stamenin aterrain-linesprojection and display the map.
map2 <- get_map(bbox2, maptype = "terrain-lines", source = "stamen")## Source : http://tile.stamen.com/terrain/5/6/11.png## Source : http://tile.stamen.com/terrain/5/7/11.png## Source : http://tile.stamen.com/terrain/5/8/11.png## Source : http://tile.stamen.com/terrain/5/6/12.png## Source : http://tile.stamen.com/terrain/5/7/12.png## Source : http://tile.stamen.com/terrain/5/8/12.pngggmap(map2)
- Build a final map that overlays the recorded observations of wolves in the lower 48 states.
ggmap(map2) +
geom_point(us_wolves, mapping=aes(x=long, y=lat), size=2)+
labs(x = "Longitude", y = "Latitude", title = "Wolves in the Lower 48")
- Use the map from #9 above, but add some aesthetics. Try to
fillandcolorby population.
ggmap(map2) +
geom_point(us_wolves, mapping=aes(x=long, y=lat, fill=pop, color=pop), size=3)+
labs(x = "Longitude", y = "Latitude", title = "Wolves in the Lower 48")
Push your final code to GitHub!
Please be sure that you check the keep md file in the knit preferences.